Public data visualisation in traffic, based on local, real-time monitoring

Quite some people have smartphones nowadays. They often use them for personal purposes like gaming, planning, and education. Several of these apps for personal use make use of sensors or services that are available on the smartphone itself. These can include sensors like the accelerometer, light sensor, temperature sensor, magnetic field sensor, and gyroscope or services like GPS, Mobile advertisements, and Activity Recognition. While these sensors and services are very useful in personal applications, they can also
be used for a larger purpose. This report discusses the implementation of a system that gathers data from several smartphones, and applies it to traffic situations, such that a public display can be created with accurate traffic
information. The research question therefore is: ”Is it possible to design a public system that can maximise throughput of cars in traffic situations,
based on local, real-time, flexible monitoring using smartphones?”
For such a system, it is needed to collect this data from these smartphones somehow, and process it to a useful conclusion that can be made visible to the individual car driver. Three main components in the system can therefore be identified: A car driver as being the client to the system, a collecting unit consisting of an access point and a webserver, and a processing unit that allows data to come in and that processes this data. By installing an application on its smartphone, the car driver automatically
sends data about its current location, velocity, and sudden decelerations to the collecting unit. The collecting unit collects all data from the smartphones of car drivers within a certain range. These collecting units can send
data through to other collecting units, so that more data can be gathered, or to a processing unit. Data that reaches this processing unit will be processed, such that the public signals in the traffic can be changed.
During the scope of this project, a prototype was made to show the workings of the system in a demonstration environment. This prototype implemented the situation of a single crossroad with traffic lights. One of
the roads (the crossing road) was hardcoded, which means that traffic was programmed to drive there, but no traffic was created there using a smartphone or collecting units. The ongoing road was used to show the workings
of the app and collecting unit. The app sends out an identifier (to identify the smartphone or tablet), and a time till it reaches the traffic light. The collecting unit gathers this data via a webserver (the app sends data to this webserver), and sends it through to the processing unit. The processing unit makes up a scheme for the traffic lights, and changes the colour of the traffic lights whenever needed. Another thing that the processing unit does
is to take a look into the future, as to predict what happens with the traffic lights. In that way, a speed can be given to car drivers at a distance of 200 metres away from the traffic light that indicates if they can still make
it through the green light with their current speed or if they should drive faster or cannot make it all anymore. The collecting unit was implemented by an arduino on which an ethernet shield was stacked to create a webserver.
The processing unit was implemented by only an arduino.
The prototype had several delays. Especially acquiring GPS data took a long time. Another thing that was measured, was the average velocity of the tablet when it was lying flat on the table. The average measured speed was about 0.4m
s without moving the tablet. This could be seen as an offset
level once the app is used in a real-life application. The maximum velocity measured during leaving the tablet flat on the table without moving it was 1m s , which is mainly (except for the offset level) a random error that cannot
be overcome.
When a request was made to the server, it depended on the time left till the intersection was reached what the chance was that a car would still make it through green light. It was shown that cars that had still the largest
distance to cover had the largest chance of getting a green light once they reached the intersection.